There has been a great deal of work on scheduler and resource management. The general approach is to maximize a cost function subject to the capacity limit and other constraints such that certain performance measures are achieved. A great deal of work has been done in the areas of funding effective cost functions, theoretical proves of the property of those functions, solving optimization problems with those cost functions with respect to different physical layer characteristics, and the correspondent algorithms based those theoretical results. For example, a widely used cost function is a utility based function. The main advantage of a utility based resource management compared to more traditional system centric criteria, such as power, outage probability and throughput, is that it can be used to evaluate to what degree a system satisfies service requirements of an user's application. G. Song and Y. Li, “Utility-Based Resource Allocation Scheduling in OFDM-Based Wireless Broadband Networks”, IEEE Communications Magazine, December 2005, pp. 127-134 (hereinafter, Song et al.) and the references therein give a good overview on state-of-the-art theories and algorithms of scheduler and resources management, especially for OFDM based systems.
However, all of the prior arts have mathematically formulated the problem on the assumptions that a system is one Basestation and all the user terminals (UEs) being considered are associated with the Basestation under study. As a result, the cost function as well as its optimization targets how to maximize a cost function with respect to some or all users in one BTS subject to the capacity limit and other constraints such that certain performance measures are achieved. Hence, the scheduler and resource management algorithms derived from above assumption and theory are for scheduling UEs in individual BTS without considering other BTSs, their corresponding schedulers, and their UEs. Mathematically, the above optimization problem is to assign radio resources in order to maximize the following cost function:
      1    M    ⁢            ∑              i        =        1            M        ⁢                  ⁢                  U        i            ⁡              (                              r            i                    ⁡                      [            n            ]                          )            
Where ri[n] is the instantaneous dates of user i at time n, Ui(•) is the corresponding utility function of user i. Again, all the users are in the same cell or being served by one BTS, and the optimization is done w.r.t. one cell or BTS.
On implementation side, traditionally, the scheduler resides in the BTS, or NodeB in 3GPP term. The scheduler is responsible for assigning radio resources to the UEs in the cell based on the available radio resources, user channel quality, user request, QoS requirements.
Additional radio resource management residing further above BTSs is responsible for handoff related resource management, such as orthogonal code assignment between the cells, application data buffer management, and so on.